In the recent past it is increasingly being observed that social media plays an important role in putting a dormant crowd into action. The most recent examples of social media fueled crowd activism are the Egyptian revolution, the Spanish May 15th movement, and the most recent Occupy Wall Street protests that spread out across many cities in the United States. The common underlying theme in all these examples of activism is the intelligent use of social media such as Facebook, Twitter, etc. to bring a crowd into action. Beneath any crowd is a network of people who are engaged towards a common goal for a crowd to perform a purposeful action. Therefore, it is desirable to be able to understand why and how the engagement of a crowd changes from time to time. For example, some movements start off great but die out soon, some movements start out small but pick up momentum and turn into a full-fledged revolutions.
Crowds can be represented as complex networks. Most real complex networks, such as telecommunication networks, are not homogeneously linked by similar type of edges. Most real complex networks display certain characteristic properties like small world phenomenon and scale free distributions. Today's hyper-connected networks of people, information, and devices pose an entirely different challenge—in order to extract the value of today's crowd networks, it is not enough to understand simply the connections of the network. It is also important to understand the activity on those connections, representing the engagement of the crowd, and how it grows or shrinks over time and under what conditions. Only then the true value of the crowd network can be extracted.
Complex network properties like clustering, shortest path lengths, etc. are traditional measures for examining properties of a graph network. In the case of a social network, the notion of ‘engagement’ or ‘activity’ between the people/nodes in the network is an important aspect. Engagement has certain properties like working towards a common goal or working in parallel on different tasks to achieve a common higher task, etc. Up until now, however, the relationship between engagement and network structures has not been well-defined. This invention provides a method for computing the value of a social network based on network entropy and for extracting the network structures that contribute to that value.